Descriptives statistics, distributions, correlations, and reliability estimates

Driving simulation metrics

Overall

collisions_overall speed_overall distance_overall
speed_overall -0.04
distance_overall 0.24 0.04
distance_overall_deviation 0.24 0.04 1.00*** 
## Some items ( 2 4 ) were negatively correlated with the total scale and 
## probably should be reversed.  
## To do this, run the function again with the 'check.keys=TRUE' optionSome items ( 2 4 ) were negatively correlated with the total scale and 
## probably should be reversed.  
## To do this, run the function again with the 'check.keys=TRUE' option
## [[1]]
## [1] "collisions = 0.83"
## 
## [[2]]
## [1] "speed = 0.88"
## 
## [[3]]
## [1] "distance = 0.17"
## 
## [[4]]
## [1] "distance_deviation = 0.17"

Fog-free periods

collisions_no_fog_overall speed_no_fog_overall
speed_no_fog_overall 0.34*
distance_no_fog_overall 0.24 -0.03 
## Some items ( 1 5 ) were negatively correlated with the total scale and 
## probably should be reversed.  
## To do this, run the function again with the 'check.keys=TRUE' option
## [[1]]
## [1] "collisions_no_fog = 0.81"
## 
## [[2]]
## [1] "speed_no_fog = 0.75"
## 
## [[3]]
## [1] "distance_no_fog = 0.04"

Fog event probe

collisions_fog_overall speed_fog_overall
speed_fog_overall -0.05
distance_fog_overall 0.27* 0.08 
## Some items ( 2 ) were negatively correlated with the total scale and 
## probably should be reversed.  
## To do this, run the function again with the 'check.keys=TRUE' option
## [[1]]
## [1] "collisions_fog = 0.66"
## 
## [[2]]
## [1] "speed_fog = 0.77"
## 
## [[3]]
## [1] "distance_fog = -0.05"

Psychometric measures

Overall

var mean_overall sd_overall mean_driver sd_driver mean_co sd_co
agreeableness 3.9189815 0.6319277 3.98 0.63 3.86 0.63
bias 3.0441277 20.5145140 3.12 19.66 2.96 21.52
confidence 60.1323343 19.0227797 58.37 19.54 61.89 18.51
congruent_errors 0.9345794 1.3823075 0.93 1.55 0.94 1.20
congruent_time 461.5544460 51.0173810 448.12 50.44 475.24 48.31
conscientiousness 3.2384259 0.7499099 3.10 0.76 3.38 0.72
discrimination 27.2903277 19.4473443 28.85 19.66 25.74 19.29
driving_years 2.7171296 4.9146936 3.28 4.75 2.16 5.06
extraversion 3.1759259 0.7138968 3.18 0.73 3.17 0.70
gaming_time 1.1231481 1.0136320 1.26 1.05 0.99 0.96
gf_accuracy 57.0882066 21.9362721 55.25 21.92 58.93 22.00
incongruent_errors 1.5420561 1.8185645 1.63 1.78 1.45 1.87
incongruent_time 532.1606055 59.6359822 514.92 62.99 549.72 50.82
inhibitory_cost 70.6061595 30.0274495 66.80 31.09 74.49 28.68
intellect 3.6018519 0.6654324 3.56 0.65 3.64 0.69
neuroticism 2.7962963 0.7323852 2.82 0.75 2.77 0.72
repeat_errors 1.6574074 1.7356193 1.78 1.66 1.54 1.82
repeat_time 920.7157111 244.1991035 930.65 241.58 910.78 248.65
resilience 3.6137500 0.3568200 3.64 0.33 3.59 0.38
switch_cost 125.2960244 144.8566483 103.81 116.58 146.79 166.82
switch_errors 2.0370370 2.1352665 2.11 2.00 1.96 2.28
switch_time 1046.0117355 304.7873181 1034.45 289.04 1057.57 322.07
wm_accuracy 43.2716049 18.5563131 40.86 19.43 45.68 17.49

agreeableness bias confidence congruent_errors congruent_time conscientiousness discrimination driving_years extraversion gaming_time gf_accuracy incongruent_errors incongruent_time inhibitory_cost intellect neuroticism repeat_errors repeat_time resilience switch_cost switch_errors switch_time
bias -0.07
confidence 0.01 0.39***
congruent_errors 0.11 0.18 0.03
congruent_time 0.03 -0.18 -0.48*** -0.11
conscientiousness 0.06 0.09 -0.05 -0.06 0.17
discrimination -0.07 -0.35*** 0.03 -0.09 -0.17 -0.01
driving_years 0.05 -0.29** -0.25* -0.09 0.28** 0.13 -0.04
extraversion -0.03 0.09 -0.03 -0.10 0.10 -0.04 -0.06 0.05
gaming_time 0.16 -0.07 0.22* -0.16 -0.17 -0.22* 0.24* -0.17 -0.18
gf_accuracy 0.07 -0.60*** 0.51*** -0.14 -0.24* -0.12 0.36*** 0.06 -0.11 0.26**
incongruent_errors 0.04 0.14 0.14 0.26** -0.52*** -0.19 0.08 -0.19 -0.04 0.07 -0.01
incongruent_time -0.03 -0.19 -0.47*** -0.21* 0.86*** 0.14 -0.13 0.26** 0.03 -0.17 -0.23* -0.41***
inhibitory_cost -0.11 -0.07 -0.11 -0.23* 0.02 -0.02 0.01 0.04 -0.11 -0.04 -0.03 0.07 0.52***
intellect -0.06 -0.05 0.20* 0.07 -0.10 -0.17 0.14 -0.04 -0.06 0.29** 0.21* 0.07 -0.09 0.00
neuroticism 0.04 -0.12 0.02 0.08 0.03 0.02 0.09 -0.04 0.00 -0.10 0.13 0.12 0.05 0.04 0.07
repeat_errors -0.11 0.16 -0.13 0.22* -0.15 -0.24* -0.16 -0.13 -0.09 -0.15 -0.27** 0.43*** -0.09 0.08 -0.02 0.03
repeat_time -0.02 -0.09 -0.37*** -0.14 0.51*** 0.10 -0.10 0.15 0.10 -0.14 -0.24* -0.25* 0.41*** -0.05 -0.03 0.17 -0.08
resilience 0.08 -0.02 0.06 -0.12 -0.09 0.14 0.05 0.06 0.17 -0.04 0.07 0.08 -0.12 -0.08 -0.10 -0.37*** -0.01 -0.15
switch_cost -0.17 -0.29** -0.25** -0.10 0.38*** -0.02 0.08 0.20* 0.09 -0.13 0.06 -0.25** 0.32*** -0.01 -0.14 -0.08 -0.11 0.17 0.08
switch_errors -0.03 0.30** -0.18 0.25** -0.01 -0.18 -0.15 -0.13 -0.02 -0.10 -0.44*** 0.30** 0.02 0.06 -0.01 0.02 0.58*** -0.12 -0.07 -0.13
switch_time -0.10 -0.21* -0.42*** -0.16 0.59*** 0.07 -0.04 0.21* 0.13 -0.17 -0.16 -0.32*** 0.48*** -0.04 -0.09 0.10 -0.12 0.88*** -0.09 0.61*** -0.16
wm_accuracy -0.03 0.04 0.36*** -0.08 -0.15 0.02 0.05 -0.13 -0.09 0.14 0.27** 0.07 -0.14 -0.03 0.35*** 0.10 -0.07 -0.14 0.06 0.07 -0.11 -0.08

Reliability overall

## [1] "resilience = 0.79"
## [1] "extraversion = 0.7"
## [1] "agreeableness = 0.59"
## [1] "conscientiousness = 0.65"
## [1] "neuroticism = 0.68"
## [1] "intellect = 0.61"
## [[1]]
## [1] "extraversion = 0.7"
## 
## [[2]]
## [1] "agreeableness = 0.59"
## 
## [[3]]
## [1] "conscientiousness = 0.65"
## 
## [[4]]
## [1] "neuroticism = 0.68"
## 
## [[5]]
## [1] "intellect = 0.61"
## [1] "RAPM accuracy = 0.83"
## [1] "RAPM confidence = 0.93"
## [1] "RAPM bias = 0.85"
## [1] "RAPM discrimination = 0.59"
## Some items ( 14 16 19 24 25 ) were negatively correlated with the total scale and 
## probably should be reversed.  
## To do this, run the function again with the 'check.keys=TRUE' option[1] "repeat errors = 0.49"
## Some items ( 1 2 6 7 10 11 12 14 16 18 20 22 ) were negatively correlated with the total scale and 
## probably should be reversed.  
## To do this, run the function again with the 'check.keys=TRUE' option[1] "switch errors = 0.39"
## [[1]]
## [1] "repeat errors = 0.49"
## 
## [[2]]
## [1] "switch errors = 0.39"
## [1] "repeat time = 0.84"
## [1] "switch time = 0.87"
## [[1]]
## [1] "repeat time = 0.84"
## 
## [[2]]
## [1] "switch time = 0.87"
## [1] "switch cost = 0.01"
## [1] "working memory accuracy = 0.7"
## Some items ( 1 4 6 7 8 9 11 12 13 14 15 16 17 19 23 25 28 30 31 32 34 40 44 45 46 49 55 56 60 63 67 71 72 79 ) were negatively correlated with the total scale and 
## probably should be reversed.  
## To do this, run the function again with the 'check.keys=TRUE' option[1] "congruent errors = 0.53"
## Some items ( 20 ) were negatively correlated with the total scale and 
## probably should be reversed.  
## To do this, run the function again with the 'check.keys=TRUE' option[1] "incongruent errors = 0.59"
## [[1]]
## [1] "congruent errors = 0.53"
## 
## [[2]]
## [1] "incongruent errors = 0.59"
## [1] "congruent time = 0.98"
## [1] "incongruent time = 0.82"
## [[1]]
## [1] "congruent time = 0.98"
## 
## [[2]]
## [1] "incongruent time = 0.82"
## [1] "inhibitory cost = 0.95"

Reliability for driver

## Some items ( 18 20 ) were negatively correlated with the total scale and 
## probably should be reversed.  
## To do this, run the function again with the 'check.keys=TRUE' option[1] "resilience = 0.77"
## [1] "extraversion = 0.76"
## [1] "agreeableness = 0.64"
## [1] "conscientiousness = 0.69"
## [1] "neuroticism = 0.68"
## [1] "intellect = 0.59"
## [[1]]
## [1] "extraversion = 0.76"
## 
## [[2]]
## [1] "agreeableness = 0.64"
## 
## [[3]]
## [1] "conscientiousness = 0.69"
## 
## [[4]]
## [1] "neuroticism = 0.68"
## 
## [[5]]
## [1] "intellect = 0.59"
## [1] "RAPM accuracy = 0.83"
## [1] "RAPM confidence = 0.94"
## [1] "RAPM bias = 0.84"
## [1] "RAPM discrimination = 0.45"
## Some items ( 2 3 4 8 9 13 14 17 19 21 23 24 ) were negatively correlated with the total scale and 
## probably should be reversed.  
## To do this, run the function again with the 'check.keys=TRUE' option[1] "switch errors = 0.3"
## Some items ( 2 3 4 5 7 8 9 10 11 12 13 14 19 21 22 26 ) were negatively correlated with the total scale and 
## probably should be reversed.  
## To do this, run the function again with the 'check.keys=TRUE' option[1] "repeat errors = 0.26"
## [[1]]
## [1] "switch errors = 0.3"
## 
## [[2]]
## [1] "repeat errors = 0.26"
## [1] "switch time = 0.88"
## [1] "repeat time = 0.86"
## [[1]]
## [1] "switch time = 0.88"
## 
## [[2]]
## [1] "repeat time = 0.86"
## [1] "switch cost = -0.21"
## Some items ( 15 ) were negatively correlated with the total scale and 
## probably should be reversed.  
## To do this, run the function again with the 'check.keys=TRUE' option[1] "working memory accuracy = 0.73"
## Some items ( 7 8 9 11 12 13 14 16 22 23 28 30 32 34 44 45 56 67 72 79 ) were negatively correlated with the total scale and 
## probably should be reversed.  
## To do this, run the function again with the 'check.keys=TRUE' option[1] "congruent errors = 0.64"
## Some items ( 1 6 11 17 20 ) were negatively correlated with the total scale and 
## probably should be reversed.  
## To do this, run the function again with the 'check.keys=TRUE' option[1] "incongruent errors = 0.56"
## [[1]]
## [1] "congruent errors = 0.64"
## 
## [[2]]
## [1] "incongruent errors = 0.56"
## [1] "congruent time = 0.99"
## [1] "incongruent time = 0.86"
## [[1]]
## [1] "congruent time = 0.99"
## 
## [[2]]
## [1] "incongruent time = 0.86"
## [1] "inhibitory cost = 0.97"

Reliability for codriver

## [1] "resilience = 0.82"
## [1] "extraversion = 0.65"
## [1] "agreeableness = 0.54"
## [1] "conscientiousness = 0.59"
## [1] "neuroticism = 0.68"
## [1] "intellect = 0.63"
## [[1]]
## [1] "extraversion = 0.65"
## 
## [[2]]
## [1] "agreeableness = 0.54"
## 
## [[3]]
## [1] "conscientiousness = 0.59"
## 
## [[4]]
## [1] "neuroticism = 0.68"
## 
## [[5]]
## [1] "intellect = 0.63"
## [1] "RAPM accuracy = 0.84"
## [1] "RAPM confidence = 0.92"
## [1] "RAPM bias = 0.85"
## [1] "RAPM discrimination = 0.72"
## Some items ( 6 9 10 11 12 14 16 17 18 19 24 25 26 ) were negatively correlated with the total scale and 
## probably should be reversed.  
## To do this, run the function again with the 'check.keys=TRUE' option[1] "repeat errors = 0.62"
## Some items ( 3 4 5 8 10 12 13 15 16 17 19 20 21 22 23 24 ) were negatively correlated with the total scale and 
## probably should be reversed.  
## To do this, run the function again with the 'check.keys=TRUE' option[1] "switch errors = 0.48"
## [[1]]
## [1] "repeat errors = 0.62"
## 
## [[2]]
## [1] "switch errors = 0.48"
## [1] "repeat time = 0.82"
## [1] "switch time = 0.87"
## [[1]]
## [1] "repeat time = 0.82"
## 
## [[2]]
## [1] "switch time = 0.87"
## [1] "switch cost = 0.09"
## [1] "working memory accuracy = 0.66"
## Some items ( 1 3 4 6 9 11 12 14 15 17 19 22 23 24 26 31 38 41 42 49 55 57 59 60 71 72 75 76 77 79 ) were negatively correlated with the total scale and 
## probably should be reversed.  
## To do this, run the function again with the 'check.keys=TRUE' option[1] "congruent errors = 0.36"
## Some items ( 2 11 19 20 ) were negatively correlated with the total scale and 
## probably should be reversed.  
## To do this, run the function again with the 'check.keys=TRUE' option[1] "incongruent errors = 0.64"
## [[1]]
## [1] "congruent errors = 0.36"
## 
## [[2]]
## [1] "incongruent errors = 0.64"
## [1] "congruent time = 0.95"
## [1] "incongruent time = 0.74"
## [[1]]
## [1] "congruent time = 0.95"
## 
## [[2]]
## [1] "incongruent time = 0.74"
## [1] "inhibitory cost = 0.93"

Communication measures

co_info_help_overall co_info_harm_overall co_instruct_help_overall co_instruct_harm_overall co_redundant_overall co_question_overall drive_question_overall drive_informs_overall
co_info_harm_overall 0.31*
co_instruct_help_overall 0.41** 0.03
co_instruct_harm_overall 0.13 0.43** 0.48***
co_redundant_overall 0.11 0.36** 0.25 0.30*
co_question_overall 0.38** 0.03 0.49*** 0.45*** 0.16
drive_question_overall 0.63*** 0.23 0.41** 0.15 0.21 0.31*
drive_informs_overall 0.52*** 0.17 0.45*** 0.35** 0.20 0.72*** 0.38**
drive_frust_overall 0.14 0.39** 0.30* 0.30* 0.33* 0.11 0.28* 0.26

Reliability estimates

## [[1]]
## [1] "co_info_help = 0.84"
## 
## [[2]]
## [1] "co_info_harm = 0.73"
## 
## [[3]]
## [1] "co_instruct_help = 0.9"
## 
## [[4]]
## [1] "co_instruct_harm = 0.52"
## 
## [[5]]
## [1] "co_redundant = 0.73"
## 
## [[6]]
## [1] "co_question = 0.8"
## 
## [[7]]
## [1] "drive_question = 0.8"
## 
## [[8]]
## [1] "drive_informs = 0.85"
## 
## [[9]]
## [1] "drive_frust = 0.89"

Given the moderate to high correlations between the communication variables, it looks like there is a multicollinearity issue. Need to reduce comms variables to a smaller number using factor analysis.

Conduct EFA to extract comms factors

Reduce variables to smaller number of factors and correlate with each other. It looks like 3 components can be extracted from the comms variables.

Correlations between original comms variables

co_info_help_overall co_info_harm_overall co_instruct_help_overall co_instruct_harm_overall co_redundant_overall co_question_overall drive_question_overall drive_informs_overall
co_info_harm_overall 0.31*
co_instruct_help_overall 0.41** 0.03
co_instruct_harm_overall 0.13 0.43** 0.48***
co_redundant_overall 0.11 0.36** 0.25 0.30*
co_question_overall 0.38** 0.03 0.49*** 0.45*** 0.16
drive_question_overall 0.63*** 0.23 0.41** 0.15 0.21 0.31*
drive_informs_overall 0.52*** 0.17 0.45*** 0.35** 0.20 0.72*** 0.38**
drive_frust_overall 0.14 0.39** 0.30* 0.30* 0.33* 0.11 0.28* 0.26

KMO and Bartlett’s test of Spherecity

## Kaiser-Meyer-Olkin factor adequacy
## Call: KMO(r = cor(pca, use = "complete.obs"))
## Overall MSA =  0.65
## MSA for each item = 
##     co_info_help_overall     co_info_harm_overall co_instruct_help_overall 
##                     0.61                     0.44                     0.67 
## co_instruct_harm_overall     co_redundant_overall      co_question_overall 
##                     0.58                     0.78                     0.69 
##   drive_question_overall    drive_informs_overall      drive_frust_overall 
##                     0.77                     0.74                     0.67
## [1] "Bartletts test of spherecity"
##      chisq      p.value df
## 1 176.0047 2.388931e-20 36

Communalities

communalities
co_info_help_overall 0.82
co_info_harm_overall 0.70
co_instruct_help_overall 0.59
co_instruct_harm_overall 0.74
co_redundant_overall 0.49
co_question_overall 0.81
drive_question_overall 0.76
drive_informs_overall 0.70
drive_frust_overall 0.52

Variance explained by the extracted components

component eigen prop_var cum_var rotation_SS_load
1 3.54 0.27 0.27 2.42
2 1.47 0.22 0.49 1.98
3 1.12 0.19 0.68 1.74
4 0.78
5 0.68
6 0.62
7 0.37
8 0.22
9 0.19

Pattern matrix

var PC1 PC2 PC3
co_question_overall 0.95
drive_informs_overall 0.73
co_instruct_help_overall 0.71
co_instruct_harm_overall 0.62 0.46 -0.33
co_info_harm_overall 0.87
drive_frust_overall 0.71
co_redundant_overall 0.68
co_info_help_overall 0.85
drive_question_overall 0.81

Component correlations matrix

##              inconsistent terrible helpful
## inconsistent         1.00     0.38    0.33
## terrible             0.38     1.00    0.17
## helpful              0.33     0.17    1.00

Check that component scores extracted using SPSS and R are the same

##               component      r
## 1 inconsistent_codriver 0.9998
## 2     terrible_codriver 0.9996
## 3      helpful_exchange 0.9997

Regression analyses

Correlations between all variables

collisions_overall speed_overall distance_overall inconsistent_codriver terrible_codriver helpful_exchange age_co_driver age_driver aus_born_co_driver aus_born_driver aus_years_co_driver aus_years_driver dic_use_co_driver dic_use_driver eng_fl_co_driver eng_fl_driver sex_co_driver sex_driver prop_female driving_years gaming_time congruent_errors congruent_time incongruent_errors incongruent_time inhibitory_cost repeat_errors repeat_time switch_errors switch_time switch_cost wm_accuracy resilience gf_accuracy confidence bias discrimination agreeableness conscientiousness extraversion intellect neuroticism driving_years_drone gaming_time_drone congruent_errors_drone congruent_time_drone incongruent_errors_drone incongruent_time_drone inhibitory_cost_drone repeat_errors_drone repeat_time_drone switch_errors_drone switch_time_drone switch_cost_drone wm_accuracy_drone resilience_drone gf_accuracy_drone confidence_drone bias_drone discrimination_drone agreeableness_drone conscientiousness_drone extraversion_drone intellect_drone
speed_overall -0.04
distance_overall 0.24 0.04
inconsistent_codriver 0.28* -0.27* 0.27*
terrible_codriver 0.32* -0.09 0.38** 0.34*
helpful_exchange 0.07 -0.40** 0.20 0.32* 0.17
age_co_driver 0.03 0.10 0.19 -0.02 -0.05 0.12
age_driver -0.01 -0.04 0.00 -0.11 0.07 0.20 -0.08
aus_born_co_driver -0.05 -0.09 0.07 0.20 -0.17 0.30* -0.12 0.02
aus_born_driver -0.03 0.01 -0.09 0.04 0.02 0.00 0.14 -0.27* -0.03
aus_years_co_driver 0.19 -0.20 0.35 0.31 -0.18 0.25 0.60** -0.06 NANA 0.07
aus_years_driver -0.01 -0.29 0.11 0.11 0.04 0.65* -0.22 0.47 0.04 NANA -0.02
dic_use_co_driver -0.40 -0.29 0.02 -0.23 -0.07 0.17 0.15 0.18 0.02 -0.22 -0.30 0.83
dic_use_driver -0.16 0.36 -0.04 -0.25 -0.09 -0.36 0.55 -0.21 -0.56 -0.72* -0.10 -0.89* 0.16
eng_fl_co_driver 0.01 -0.22 0.11 0.18 -0.31* 0.30* 0.06 0.10 0.53*** 0.01 0.57** 0.23 NANA -0.04
eng_fl_driver -0.27 0.15 -0.10 -0.17 -0.23 -0.03 0.10 -0.19 0.09 0.40** -0.09 -0.12 -0.36 NANA 0.11
sex_co_driver -0.22 0.08 -0.08 0.06 -0.04 0.05 -0.11 -0.07 0.07 0.14 -0.12 0.14 0.18 -0.37 -0.04 0.09
sex_driver -0.28* 0.58*** 0.07 -0.21 -0.13 -0.24 0.11 -0.19 0.06 0.00 -0.01 -0.41 -0.12 0.19 -0.20 0.04 0.22
prop_female 0.32* -0.47*** 0.00 0.13 0.12 0.15 -0.02 0.18 -0.08 -0.07 0.08 0.34 -0.03 0.03 0.16 -0.07 -0.70*** -0.85***
driving_years -0.08 -0.06 -0.03 -0.10 0.03 0.19 -0.07 0.95*** -0.03 -0.25 -0.12 0.51 0.53* -0.16 0.16 -0.07 -0.04 -0.26 0.22
gaming_time 0.12 -0.01 0.24 0.05 0.09 0.19 -0.07 -0.05 0.08 -0.08 0.02 0.04 0.24 0.06 -0.13 -0.39** 0.15 0.29* -0.29* -0.16
congruent_errors 0.07 -0.13 -0.22 -0.17 -0.22 -0.18 0.00 -0.19 0.03 0.06 0.05 0.22 -0.29 0.25 0.15 0.19 -0.08 -0.04 0.07 -0.17 -0.20
congruent_time 0.04 -0.11 -0.18 0.01 0.00 0.08 -0.24 0.28* 0.10 -0.04 -0.17 0.37 -0.14 -0.20 0.09 -0.09 -0.09 -0.36** 0.31* 0.32* -0.29* -0.11
incongruent_errors 0.37** -0.01 0.34* 0.10 -0.01 0.03 0.42** -0.20 0.00 0.00 0.43* -0.05 -0.01 -0.01 0.12 0.01 0.01 0.08 -0.06 -0.23 0.12 0.26 -0.47***
incongruent_time 0.15 -0.03 -0.15 0.02 0.03 0.05 -0.17 0.25 0.09 -0.01 -0.22 0.21 -0.22 0.02 0.02 -0.07 -0.07 -0.36** 0.30* 0.28* -0.25 -0.22 0.87*** -0.38**
inhibitory_cost 0.24 0.11 0.00 0.01 0.06 -0.02 0.04 0.05 0.02 0.05 -0.21 -0.35 -0.26 0.71* -0.11 0.00 0.01 -0.14 0.09 0.06 -0.03 -0.27 0.15 0.00 0.61***
repeat_errors 0.21 -0.04 0.11 0.10 0.08 0.06 0.33* -0.21 -0.20 0.00 0.54** -0.18 -0.11 0.15 0.04 0.05 0.06 0.01 -0.04 -0.19 -0.19 0.21 -0.07 0.47*** -0.03 0.04
repeat_time 0.09 -0.05 -0.04 0.15 0.14 0.06 -0.18 0.09 0.10 0.08 -0.30 0.32 -0.38 -0.34 0.02 -0.05 0.04 -0.21 0.13 0.14 -0.20 -0.13 0.62*** -0.28* 0.48*** -0.03 -0.10
switch_errors 0.24 -0.05 -0.04 -0.12 0.00 -0.23 0.09 -0.25 -0.26 0.23 0.29 -0.14 -0.19 -0.01 0.04 0.24 -0.08 -0.19 0.18 -0.22 -0.31* 0.36** 0.06 0.27 0.02 -0.07 0.43** -0.06
switch_time 0.02 0.00 -0.17 0.10 0.13 0.01 -0.17 0.16 0.02 0.02 -0.36 0.33 -0.42 -0.41 -0.11 -0.05 -0.02 -0.13 0.11 0.20 -0.24 -0.21 0.70*** -0.39** 0.57*** 0.01 -0.14 0.92*** -0.08
switch_cost -0.14 0.11 -0.34* -0.07 0.04 -0.09 -0.04 0.20 -0.14 -0.11 -0.34 0.14 -0.29 -0.24 -0.30* -0.03 -0.12 0.10 -0.01 0.21 -0.18 -0.25 0.45*** -0.38** 0.41** 0.10 -0.13 0.21 -0.06 0.58***
wm_accuracy -0.12 -0.06 0.11 0.17 0.10 0.03 -0.13 0.02 0.06 -0.24 -0.14 -0.21 0.08 0.52 0.09 -0.04 0.15 0.13 -0.18 0.02 0.18 -0.11 -0.15 0.03 -0.08 0.08 -0.11 -0.24 -0.05 -0.19 0.03
resilience 0.02 0.20 -0.03 0.16 0.24 0.11 -0.03 0.19 -0.20 -0.21 0.35 0.17 0.32 0.32 -0.09 -0.39** -0.03 0.22 -0.15 0.13 0.00 -0.14 -0.05 0.09 -0.08 -0.08 0.25 -0.21 -0.09 -0.09 0.21 0.26
gf_accuracy -0.14 0.15 0.16 0.12 0.11 0.10 0.09 0.20 0.03 -0.19 -0.13 -0.26 0.24 0.26 -0.07 -0.20 0.09 0.34* -0.29* 0.20 0.40** -0.25 -0.32* -0.13 -0.21 0.10 -0.26 -0.18 -0.45*** -0.15 0.02 0.30* 0.12
confidence -0.10 0.06 0.20 0.14 0.10 -0.05 0.14 -0.19 -0.21 -0.03 0.14 -0.43 0.17 0.45 -0.10 -0.14 -0.06 0.20 -0.12 -0.22 0.38** -0.08 -0.57*** 0.00 -0.49*** -0.07 -0.21 -0.32* -0.18 -0.39** -0.29* 0.35* 0.02 0.56***
bias 0.06 -0.11 0.03 0.00 -0.02 -0.16 0.05 -0.42** -0.24 0.18 0.38 -0.24 -0.15 0.29 -0.03 0.09 -0.15 -0.17 0.21 -0.44*** -0.07 0.20 -0.21 0.14 -0.26 -0.18 0.08 -0.12 0.32* -0.22 -0.31* 0.01 -0.10 -0.56*** 0.37**
discrimination -0.06 0.21 0.08 -0.14 -0.13 -0.06 0.07 -0.10 0.01 0.10 -0.12 -0.23 0.04 -0.53 -0.17 -0.06 0.34* 0.38** -0.46*** -0.15 0.32* -0.14 -0.22 0.13 -0.20 -0.04 -0.13 -0.02 -0.06 -0.01 0.01 0.10 -0.05 0.32* 0.11 -0.25
agreeableness 0.12 -0.33* 0.05 0.33* 0.15 0.45*** -0.15 0.13 0.32* 0.10 0.13 0.64* 0.12 -0.51 0.37** -0.05 -0.01 -0.29* 0.22 0.14 0.13 0.15 0.06 0.06 0.01 -0.07 -0.07 0.07 0.03 -0.04 -0.24 0.06 0.08 0.17 0.04 -0.15 -0.04
conscientiousness -0.15 -0.07 0.05 0.02 0.05 0.02 -0.21 0.15 0.05 0.05 -0.10 0.56* 0.34 -0.60 0.09 -0.09 -0.08 -0.03 0.07 0.15 -0.20 0.09 0.19 -0.11 0.13 -0.05 -0.15 0.06 0.00 0.04 -0.03 0.03 0.24 -0.13 -0.15 0.00 0.00 0.28*
extraversion -0.16 0.00 -0.08 0.12 -0.11 0.17 0.11 -0.01 -0.03 0.23 0.06 0.12 -0.29 -0.41 -0.10 0.23 -0.03 -0.19 0.15 0.07 -0.33* -0.17 0.12 -0.03 0.06 -0.07 -0.05 0.26 0.03 0.26 0.11 -0.15 -0.04 -0.23 -0.19 0.07 0.09 -0.11 -0.08
intellect 0.04 0.04 -0.08 -0.22 0.03 -0.11 -0.06 0.01 0.11 -0.01 -0.21 -0.20 -0.09 0.67* 0.01 0.08 0.05 0.06 -0.07 0.01 0.23 0.28* -0.17 0.05 -0.10 0.07 -0.08 -0.12 0.05 -0.15 -0.13 0.40** -0.02 0.19 0.19 -0.02 0.11 0.07 -0.28* -0.24
neuroticism 0.12 0.04 0.26 -0.05 -0.12 -0.18 0.09 -0.12 -0.02 -0.08 0.03 -0.23 0.00 0.49 0.17 0.08 -0.18 -0.05 0.13 -0.04 -0.24 0.16 0.00 0.18 0.05 0.11 0.07 0.14 0.22 0.00 -0.28* -0.03 -0.34* 0.02 -0.03 -0.06 0.09 -0.04 0.06 0.07 0.00
driving_years_drone 0.05 0.11 0.21 0.03 -0.12 0.11 0.95*** -0.07 -0.07 0.09 0.66*** -0.23 -0.47 0.41 0.18 0.09 -0.06 0.15 -0.07 -0.09 -0.07 0.03 -0.25 0.43** -0.19 0.02 0.32* -0.20 0.07 -0.21 -0.11 -0.12 0.08 0.07 0.14 0.06 0.12 -0.07 -0.15 0.06 -0.09 0.10
gaming_time_drone -0.05 0.00 -0.05 -0.10 -0.03 -0.05 -0.19 0.21 0.07 0.05 -0.28 0.19 0.24 0.14 -0.08 -0.03 0.32* 0.04 -0.20 0.20 0.21 -0.02 -0.21 -0.01 -0.11 0.12 0.04 -0.14 0.02 -0.22 -0.26 0.12 0.04 0.13 -0.04 -0.19 0.04 0.13 -0.12 0.01 0.24 0.03 -0.22
congruent_errors_drone -0.06 0.07 0.02 -0.09 -0.18 0.07 -0.02 0.13 0.12 -0.18 -0.37 0.35 -0.09 0.07 0.08 0.23 0.02 0.08 -0.07 0.21 -0.07 0.07 0.07 0.01 0.06 0.01 -0.22 0.03 -0.31* 0.03 0.02 0.13 -0.16 -0.01 -0.07 -0.06 0.14 -0.18 0.05 0.14 0.05 0.08 0.00 -0.12
congruent_time_drone -0.06 -0.03 -0.06 -0.12 -0.26 -0.14 0.32* -0.06 -0.08 0.14 0.34 -0.39 -0.45 0.23 0.22 0.00 -0.19 0.00 0.10 -0.06 -0.03 0.20 -0.24 0.23 -0.18 0.02 0.11 -0.20 0.21 -0.25 -0.19 0.04 -0.09 -0.01 0.17 0.17 -0.19 -0.02 -0.02 -0.03 0.03 -0.02 0.34* 0.02 -0.12
incongruent_errors_drone -0.07 0.04 0.07 0.06 0.11 0.01 -0.14 -0.06 -0.11 0.06 -0.33 0.41 0.27 0.14 -0.21 0.14 0.30* 0.11 -0.24 0.01 0.09 0.02 0.16 -0.08 0.05 -0.15 -0.03 0.14 -0.02 0.11 -0.02 -0.09 -0.11 0.14 -0.02 -0.17 0.26 0.04 0.01 -0.10 0.06 0.16 -0.16 0.00 0.27 -0.59***
incongruent_time_drone 0.01 -0.07 -0.02 -0.12 -0.28* -0.14 0.32* -0.10 0.00 0.07 0.43 -0.26 -0.35 0.42 0.21 -0.03 -0.23 0.05 0.09 -0.13 0.04 0.40** -0.31* 0.38** -0.30* -0.10 0.06 -0.25 0.23 -0.32* -0.27* 0.09 0.06 0.04 0.17 0.12 -0.11 0.17 0.10 -0.13 0.14 0.03 0.35* 0.01 -0.22 0.83*** -0.47***
inhibitory_cost_drone 0.11 -0.07 0.06 -0.01 -0.05 -0.02 0.03 -0.08 0.13 -0.12 0.11 0.07 0.24 0.44 0.00 -0.05 -0.09 0.08 -0.01 -0.14 0.11 0.37** -0.15 0.29* -0.23 -0.22 -0.08 -0.10 0.06 -0.15 -0.17 0.10 0.24 0.09 0.03 -0.07 0.12 0.34* 0.22 -0.18 0.21 0.09 0.05 -0.02 -0.19 -0.21 0.16 0.37**
repeat_errors_drone 0.00 0.01 0.06 0.17 0.25 0.04 -0.12 -0.03 0.04 0.07 -0.36 0.57* 0.25 -0.19 -0.05 0.14 0.06 0.11 -0.11 -0.01 -0.05 0.21 -0.02 0.13 -0.06 -0.08 0.09 0.04 -0.04 -0.01 -0.10 0.14 0.17 0.13 -0.08 -0.23 0.19 0.20 0.27* -0.05 0.10 0.27 -0.09 -0.13 0.23 -0.20 0.39** -0.11 0.13
repeat_time_drone 0.01 0.26 0.13 -0.07 -0.02 0.03 0.23 -0.08 -0.15 -0.07 0.15 -0.33 0.02 0.71* 0.01 0.01 -0.13 0.28* -0.13 -0.07 0.04 -0.01 -0.21 0.23 -0.19 -0.04 0.25 -0.08 0.16 -0.11 -0.10 0.05 0.19 0.14 0.19 0.03 -0.26 -0.12 -0.05 -0.08 -0.04 0.05 0.15 -0.09 -0.16 0.46*** -0.22 0.41** -0.05 -0.07
switch_errors_drone -0.21 -0.15 0.17 0.19 0.07 0.03 -0.14 0.03 0.20 0.12 -0.29 0.48 0.28 -0.29 0.22 0.22 0.21 0.03 -0.14 0.08 -0.09 0.35* -0.09 0.15 -0.24 -0.35** 0.06 0.01 0.02 -0.09 -0.25 0.15 0.04 0.10 -0.08 -0.19 0.17 0.31* 0.13 -0.05 0.16 0.29* -0.06 0.08 0.15 -0.07 0.32* 0.05 0.21 0.70*** -0.17
switch_time_drone -0.01 0.31* 0.09 0.01 -0.12 0.06 0.28* -0.06 -0.01 0.05 0.32 -0.40 -0.17 0.42 0.22 0.06 -0.12 0.26 -0.13 -0.04 0.06 -0.05 -0.20 0.27* -0.15 0.03 0.21 -0.03 0.09 -0.08 -0.13 0.09 0.10 0.19 0.24 0.03 -0.15 0.02 -0.02 -0.02 -0.02 0.04 0.24 -0.10 -0.11 0.51*** -0.25 0.43** -0.11 -0.10 0.86*** -0.22
switch_cost_drone -0.04 0.20 -0.01 0.11 -0.21 0.08 0.20 0.00 0.20 0.21 0.46* -0.37 -0.60* -0.16 0.42** 0.09 -0.04 0.09 -0.04 0.04 0.07 -0.08 -0.07 0.19 0.00 0.12 0.04 0.07 -0.06 0.01 -0.11 0.10 -0.10 0.16 0.18 0.01 0.10 0.22 0.04 0.08 0.03 0.00 0.23 -0.06 0.03 0.30* -0.16 0.21 -0.13 -0.09 0.17 -0.17 0.65***
wm_accuracy_drone -0.05 0.09 -0.12 -0.16 0.12 -0.12 -0.24 -0.02 -0.21 -0.10 -0.54** -0.35 0.33 0.01 -0.19 -0.10 0.18 0.02 -0.11 0.02 0.03 -0.23 0.05 -0.11 0.02 -0.03 -0.05 0.01 -0.01 0.04 0.07 0.15 0.01 0.07 0.08 0.00 -0.03 -0.07 -0.03 -0.06 0.03 -0.02 -0.26 0.13 -0.03 -0.24 0.13 -0.33* -0.19 -0.01 -0.03 -0.16 0.02 0.07
resilience_drone -0.04 -0.22 0.18 0.14 0.19 0.28 -0.10 0.15 0.02 0.05 -0.17 0.50 0.43 -0.63 -0.01 -0.30* 0.07 -0.19 0.11 0.10 0.26 -0.24 0.07 -0.16 -0.08 -0.26 -0.26 0.27 -0.28 0.26 0.09 -0.05 -0.11 0.04 0.12 0.07 0.17 0.12 0.06 0.12 -0.08 -0.22 -0.11 -0.11 -0.10 -0.10 0.07 -0.14 -0.07 -0.19 -0.12 -0.07 -0.09 0.01 -0.11
gf_accuracy_drone 0.16 -0.03 -0.01 -0.17 -0.07 -0.07 0.01 -0.19 -0.08 0.14 -0.14 0.37 0.25 -0.53 -0.12 0.09 0.08 -0.15 0.06 -0.13 0.07 -0.18 0.11 -0.03 0.22 0.26 0.03 0.09 0.14 0.09 0.04 -0.19 -0.36* -0.12 -0.08 0.06 0.09 -0.04 -0.20 0.11 -0.10 -0.07 -0.05 0.14 0.00 -0.23 0.13 -0.33* -0.20 -0.27* -0.29* -0.43** -0.19 0.06 0.23 0.04
confidence_drone 0.19 -0.05 -0.11 0.12 0.23 -0.09 -0.24 -0.13 -0.16 -0.02 -0.57** 0.42 0.27 -0.18 -0.30* -0.21 0.19 -0.18 0.02 -0.13 -0.04 -0.06 0.24 -0.20 0.24 0.10 -0.12 0.21 -0.03 0.25 0.18 -0.08 -0.07 -0.26 -0.15 0.14 -0.03 -0.12 -0.03 -0.03 -0.06 -0.15 -0.26 0.08 0.19 -0.47*** 0.31* -0.55*** -0.18 -0.05 -0.41** -0.18 -0.46*** -0.27 0.36** 0.12 0.45***
bias_drone 0.01 -0.02 -0.08 0.28* 0.27* -0.01 -0.22 0.09 -0.06 -0.16 -0.37 -0.17 -0.07 0.57 -0.13 -0.27* 0.08 0.00 -0.04 0.03 -0.10 0.13 0.10 -0.15 -0.01 -0.19 -0.13 0.09 -0.17 0.12 0.11 0.13 0.34* -0.10 -0.05 0.06 -0.12 -0.06 0.18 -0.14 0.05 -0.05 -0.17 -0.08 0.16 -0.17 0.13 -0.13 0.05 0.23 -0.06 0.29* -0.20 -0.29* 0.08 0.06 -0.64*** 0.40**
discrimination_drone 0.04 -0.07 0.05 -0.19 -0.21 0.01 0.05 0.13 0.21 -0.02 0.15 0.26 0.37 -0.50 -0.05 0.01 0.09 -0.09 0.01 0.12 0.17 -0.15 0.07 0.15 0.10 0.08 -0.14 0.09 0.00 0.05 -0.07 -0.11 -0.27 -0.08 -0.15 -0.06 0.28* 0.19 0.13 0.12 -0.07 -0.11 0.04 0.14 -0.03 -0.08 0.03 -0.02 0.10 -0.20 -0.20 -0.24 -0.07 0.16 0.01 0.13 0.42** -0.03 -0.45***
agreeableness_drone -0.01 0.01 0.06 0.14 0.08 0.03 -0.10 0.11 0.10 -0.25 0.05 0.05 -0.09 0.52 0.08 -0.07 0.06 0.04 -0.06 0.05 0.21 -0.11 -0.03 -0.10 0.06 0.17 0.08 -0.12 -0.10 -0.11 -0.03 0.12 -0.04 0.10 0.03 -0.08 -0.09 -0.14 -0.13 -0.26 -0.06 -0.16 -0.05 0.17 0.07 0.06 0.02 -0.01 -0.12 -0.16 -0.11 -0.09 -0.14 -0.11 -0.11 0.05 -0.01 -0.01 0.01 -0.12
conscientiousness_drone 0.03 0.09 -0.10 0.03 0.06 0.08 0.12 0.11 -0.17 -0.07 0.05 -0.51 -0.39 0.15 -0.25 -0.12 0.06 0.06 -0.08 0.03 -0.10 -0.30* 0.06 -0.01 0.11 0.14 0.00 -0.06 -0.14 0.08 0.31* -0.14 0.28 -0.07 -0.10 -0.02 -0.07 -0.19 -0.07 0.05 -0.24 -0.35* 0.15 -0.21 -0.29* 0.06 -0.26 0.03 -0.04 -0.31* 0.16 -0.33* 0.08 -0.07 -0.04 0.08 -0.15 0.03 0.17 0.01 -0.14
extraversion_drone -0.16 -0.08 0.04 0.15 0.08 0.16 0.04 -0.05 0.06 -0.03 0.03 0.14 0.30 -0.12 0.09 -0.33* -0.02 -0.04 0.04 -0.03 0.07 -0.14 0.25 -0.23 0.12 -0.16 0.00 0.13 -0.05 0.19 0.22 0.02 0.23 0.07 -0.01 -0.09 0.15 -0.03 0.07 0.25 -0.12 -0.18 0.03 -0.02 -0.01 0.09 -0.05 0.00 -0.15 -0.14 -0.05 -0.06 0.00 0.09 -0.02 0.35* 0.01 0.16 0.12 -0.23 0.06 0.01
intellect_drone 0.02 -0.08 0.14 -0.11 0.29* -0.13 -0.02 -0.01 -0.13 -0.01 -0.06 -0.24 0.61* 0.30 -0.25 -0.14 0.03 0.00 -0.02 0.00 0.13 -0.18 0.04 -0.05 0.16 0.26 0.11 -0.18 0.18 -0.16 -0.02 0.16 0.07 0.18 0.07 -0.13 -0.08 0.07 0.00 -0.17 0.08 0.01 -0.08 0.38** -0.19 -0.07 0.09 -0.12 -0.09 0.05 0.06 -0.06 -0.04 -0.17 0.29* -0.15 0.23 0.20 -0.07 0.18 -0.17 -0.10 0.11
neuroticism_drone 0.05 0.27* 0.13 -0.10 -0.06 -0.26 0.02 0.00 -0.08 -0.03 -0.28 -0.06 0.09 0.12 -0.11 -0.05 -0.15 0.17 -0.04 0.03 0.06 0.04 0.03 0.04 0.11 0.18 0.00 -0.04 0.09 -0.05 -0.03 -0.11 -0.09 0.19 0.09 -0.13 -0.12 -0.10 0.12 -0.24 -0.06 0.15 -0.04 0.04 -0.04 0.10 0.06 0.08 -0.03 -0.01 0.20 -0.16 0.19 0.07 0.26 -0.41** 0.25 0.09 -0.18 0.09 0.11 -0.02 -0.08 0.15

Predicting the communication factors

Which variables sig. correlate with the communication factors?

rowname inconsistent_codriver
agreeableness 0.33
bias_drone 0.28
rowname terrible_codriver
bias_drone 0.27
eng_fl_co_driver -0.31
incongruent_time_drone -0.28
intellect_drone 0.29
rowname helpful_exchange
agreeableness 0.45
aus_born_co_driver 0.30
eng_fl_co_driver 0.30

Inconsistent codriver

## [1] "DV = inconsistent_codriver"
## 
## Call:
## lm(formula = fm, data = var)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1.63493 -0.58181 -0.01837  0.54393  2.70811 
## 
## Coefficients:
##                        Estimate Std. Error t value Pr(>|t|)   
## (Intercept)          -5.360e-16  1.243e-01   0.000  1.00000   
## scale(agreeableness)  3.448e-01  1.257e-01   2.742  0.00839 **
## scale(bias_drone)     3.026e-01  1.257e-01   2.406  0.01977 * 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.9134 on 51 degrees of freedom
## Multiple R-squared:  0.1971, Adjusted R-squared:  0.1656 
## F-statistic: 6.261 on 2 and 51 DF,  p-value: 0.003704
## 
##               zero_order partial part
## agreeableness       0.33    0.36 0.34
## bias_drone          0.28    0.32 0.30

## 
## Call:
## omcdiag(x = vars %>% select(n), y = vars %>% select(dv))
## 
## 
## Overall Multicollinearity Diagnostics
## 
##                        MC Results detection
## Determinant |X'X|:         0.9959         0
## Farrar Chi-Square:         0.2100         0
## Red Indicator:             0.0638         0
## Sum of Lambda Inverse:     2.0082         0
## Theil's Method:           -0.1890         0
## Condition Number:         12.8855         0
## 
## 1 --> COLLINEARITY is detected by the test 
## 0 --> COLLINEARITY is not detected by the test
## 
## 
## Call:
## imcdiag(x = vars %>% select(n), y = vars %>% select(dv))
## 
## 
## All Individual Multicollinearity Diagnostics Result
## 
##                  VIF    TOL     Wi  Fi Leamer   CVIF Klein   IND1 IND2
## agreeableness 1.0041 0.9959 0.2124 Inf  0.998 0.9888     0 0.0192    1
## bias_drone    1.0041 0.9959 0.2124 Inf  0.998 0.9888     0 0.0192    1
## 
## 1 --> COLLINEARITY is detected by the test 
## 0 --> COLLINEARITY is not detected by the test
## 
## * all coefficients have significant t-ratios
## 
## R-square of y on all x: 0.1971 
## 
## * use method argument to check which regressors may be the reason of collinearity
## ===================================

Helpful exchange

## [1] "DV = helpful_exchange"
## 
## Call:
## lm(formula = fm, data = var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.7791 -0.5876  0.0831  0.5370  1.7204 
## 
## Coefficients:
##                             Estimate Std. Error t value Pr(>|t|)   
## (Intercept)               -1.027e-16  1.221e-01   0.000  1.00000   
## scale(agreeableness)       3.777e-01  1.342e-01   2.814  0.00697 **
## scale(aus_born_co_driver)  1.334e-01  1.477e-01   0.903  0.37074   
## scale(eng_fl_co_driver)    9.313e-02  1.505e-01   0.619  0.53878   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.8972 on 50 degrees of freedom
## Multiple R-squared:  0.2406, Adjusted R-squared:  0.195 
## F-statistic: 5.279 on 3 and 50 DF,  p-value: 0.003056
## 
##                    zero_order partial part
## agreeableness            0.45    0.37 0.35
## aus_born_co_driver       0.30    0.13 0.11
## eng_fl_co_driver         0.30    0.09 0.08

## 
## Call:
## omcdiag(x = vars %>% select(n), y = vars %>% select(dv))
## 
## 
## Overall Multicollinearity Diagnostics
## 
##                        MC Results detection
## Determinant |X'X|:         0.6020         0
## Farrar Chi-Square:        25.9666         1
## Red Indicator:             0.4179         0
## Sum of Lambda Inverse:     4.1129         0
## Theil's Method:            0.3086         0
## Condition Number:         17.7600         0
## 
## 1 --> COLLINEARITY is detected by the test 
## 0 --> COLLINEARITY is not detected by the test
## 
## 
## Call:
## imcdiag(x = vars %>% select(n), y = vars %>% select(dv))
## 
## 
## All Individual Multicollinearity Diagnostics Result
## 
##                       VIF    TOL      Wi      Fi Leamer   CVIF Klein   IND1
## agreeableness      1.1857 0.8434  4.7358  9.6573 0.9184 1.4793     0 0.0331
## aus_born_co_driver 1.4364 0.6962 11.1277 22.6918 0.8344 1.7921     1 0.0273
## eng_fl_co_driver   1.4908 0.6708 12.5161 25.5230 0.8190 1.8600     1 0.0263
##                      IND2
## agreeableness      0.5950
## aus_born_co_driver 1.1542
## eng_fl_co_driver   1.2508
## 
## 1 --> COLLINEARITY is detected by the test 
## 0 --> COLLINEARITY is not detected by the test
## 
## aus_born_co_driver , eng_fl_co_driver , coefficient(s) are non-significant may be due to multicollinearity
## 
## R-square of y on all x: 0.2406 
## 
## * use method argument to check which regressors may be the reason of collinearity
## ===================================

Terrible codriver

## [1] "DV = terrible_codriver"
## 
## Call:
## lm(formula = fm, data = var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.4445 -0.4996 -0.0833  0.3611  2.7814 
## 
## Coefficients:
##                               Estimate Std. Error t value Pr(>|t|)  
## (Intercept)                   -0.06152    0.11005  -0.559   0.5788  
## scale(bias_drone)              0.25612    0.11237   2.279   0.0271 *
## scale(eng_fl_co_driver)       -0.10096    0.11857  -0.851   0.3987  
## scale(incongruent_time_drone) -0.16625    0.11456  -1.451   0.1532  
## scale(intellect_drone)         0.18504    0.11547   1.603   0.1156  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.8007 on 48 degrees of freedom
##   (1 observation deleted due to missingness)
## Multiple R-squared:  0.2261, Adjusted R-squared:  0.1617 
## F-statistic: 3.507 on 4 and 48 DF,  p-value: 0.01367
## 
##                        zero_order partial  part
## bias_drone                   0.32    0.31  0.29
## eng_fl_co_driver            -0.24   -0.12 -0.11
## incongruent_time_drone      -0.28   -0.21 -0.18
## intellect_drone              0.24    0.23  0.20

## 
## Call:
## omcdiag(x = vars %>% select(n), y = vars %>% select(dv))
## 
## 
## Overall Multicollinearity Diagnostics
## 
##                        MC Results detection
## Determinant |X'X|:         0.8676         0
## Farrar Chi-Square:         7.0747         0
## Red Indicator:             0.1562         0
## Sum of Lambda Inverse:     4.2872         0
## Theil's Method:           -0.4124         0
## Condition Number:         32.7085         1
## 
## 1 --> COLLINEARITY is detected by the test 
## 0 --> COLLINEARITY is not detected by the test
## 
## 
## Call:
## imcdiag(x = vars %>% select(n), y = vars %>% select(dv))
## 
## 
## All Individual Multicollinearity Diagnostics Result
## 
##                           VIF    TOL     Wi     Fi Leamer   CVIF Klein   IND1
## bias_drone             1.0433 0.9585 0.7069 1.0820 0.9790 1.1483     0 0.0587
## eng_fl_co_driver       1.1102 0.9008 1.7995 2.7543 0.9491 1.2219     0 0.0551
## incongruent_time_drone 1.0644 0.9395 1.0519 1.6100 0.9693 1.1715     0 0.0575
## intellect_drone        1.0693 0.9352 1.1324 1.7333 0.9670 1.1770     0 0.0573
##                          IND2
## bias_drone             0.6237
## eng_fl_co_driver       1.4920
## incongruent_time_drone 0.9096
## intellect_drone        0.9748
## 
## 1 --> COLLINEARITY is detected by the test 
## 0 --> COLLINEARITY is not detected by the test
## 
## eng_fl_co_driver , incongruent_time_drone , intellect_drone , coefficient(s) are non-significant may be due to multicollinearity
## 
## R-square of y on all x: 0.2261 
## 
## * use method argument to check which regressors may be the reason of collinearity
## ===================================

Predicting the driving-simulation metrics

Which variables sig. correlate with the driving-simulation metrics?

rowname collisions_overall
incongruent_errors 0.37
inconsistent_codriver 0.28
prop_female 0.32
sex_driver -0.28
terrible_codriver 0.32
rowname speed_overall
agreeableness -0.33
helpful_exchange -0.40
inconsistent_codriver -0.27
neuroticism_drone 0.27
prop_female -0.47
sex_driver 0.58
switch_time_drone 0.31
rowname distance_overall
incongruent_errors 0.34
inconsistent_codriver 0.27
switch_cost -0.34
terrible_codriver 0.38

Collisions overall

## [1] "DV = collisions_overall"
## 
## Call:
## lm(formula = fm, data = var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -137.26  -49.20  -13.43   33.23  292.60 
## 
## Coefficients:
##                              Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                    165.46      12.36  13.384   <2e-16 ***
## scale(incongruent_errors)       40.91      12.59   3.248   0.0021 ** 
## scale(inconsistent_codriver)    13.29      13.40   0.992   0.3261    
## scale(prop_female)              32.52      12.65   2.570   0.0133 *  
## scale(terrible_codriver)        27.36      13.31   2.056   0.0451 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 90.85 on 49 degrees of freedom
## Multiple R-squared:  0.3515, Adjusted R-squared:  0.2985 
## F-statistic: 6.639 on 4 and 49 DF,  p-value: 0.000237
## 
##                       zero_order partial part
## incongruent_errors          0.37    0.42 0.37
## inconsistent_codriver       0.28    0.14 0.11
## prop_female                 0.32    0.34 0.30
## terrible_codriver           0.32    0.28 0.24

## 
## Call:
## omcdiag(x = vars %>% select(n), y = vars %>% select(dv))
## 
## 
## Overall Multicollinearity Diagnostics
## 
##                        MC Results detection
## Determinant |X'X|:         0.8516         0
## Farrar Chi-Square:         8.1628         0
## Red Indicator:             0.1618         0
## Sum of Lambda Inverse:     4.3369         0
## Theil's Method:           -0.7556         0
## Condition Number:          4.9025         0
## 
## 1 --> COLLINEARITY is detected by the test 
## 0 --> COLLINEARITY is not detected by the test
## 
## 
## Call:
## imcdiag(x = vars %>% select(n), y = vars %>% select(dv))
## 
## 
## All Individual Multicollinearity Diagnostics Result
## 
##                          VIF    TOL     Wi     Fi Leamer   CVIF Klein   IND1
## incongruent_errors    1.0185 0.9818 0.3085 0.4720 0.9909 1.1464     0 0.0589
## inconsistent_codriver 1.1533 0.8671 2.5545 3.9084 0.9312 1.2981     0 0.0520
## prop_female           1.0281 0.9726 0.4688 0.7173 0.9862 1.1572     0 0.0584
## terrible_codriver     1.1370 0.8795 2.2830 3.4931 0.9378 1.2798     0 0.0528
##                         IND2
## incongruent_errors    0.2432
## inconsistent_codriver 1.7785
## prop_female           0.3661
## terrible_codriver     1.6122
## 
## 1 --> COLLINEARITY is detected by the test 
## 0 --> COLLINEARITY is not detected by the test
## 
## inconsistent_codriver , coefficient(s) are non-significant may be due to multicollinearity
## 
## R-square of y on all x: 0.3515 
## 
## * use method argument to check which regressors may be the reason of collinearity
## ===================================
## [[1]]
## 
## Call:
## lm(formula = fm, data = var_std)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -142.87  -46.20  -14.95   31.50  290.29 
## 
## Coefficients:
##                                   Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                        165.054     12.583  13.118  < 2e-16 ***
## inconsistent_codriver               12.771     13.681   0.933  0.35524    
## prop_female                         32.188     12.840   2.507  0.01562 *  
## incongruent_errors                  40.731     12.734   3.199  0.00245 ** 
## terrible_codriver                   27.239     13.444   2.026  0.04832 *  
## inconsistent_codriver:prop_female    3.322     12.887   0.258  0.79765    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 91.73 on 48 degrees of freedom
## Multiple R-squared:  0.3524, Adjusted R-squared:  0.2849 
## F-statistic: 5.224 on 5 and 48 DF,  p-value: 0.0006588
## 
## 
## [[2]]
## 
## Call:
## lm(formula = fm, data = var_std)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -135.24  -55.10  -12.84   34.41  291.74 
## 
## Coefficients:
##                               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                    166.559     12.594  13.225  < 2e-16 ***
## terrible_codriver               29.824     14.069   2.120  0.03922 *  
## prop_female                     32.376     12.743   2.541  0.01435 *  
## incongruent_errors              41.449     12.717   3.259  0.00206 ** 
## inconsistent_codriver           13.386     13.495   0.992  0.32621    
## terrible_codriver:prop_female   -9.455     16.497  -0.573  0.56922    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 91.48 on 48 degrees of freedom
## Multiple R-squared:  0.3559, Adjusted R-squared:  0.2888 
## F-statistic: 5.304 on 5 and 48 DF,  p-value: 0.0005859
## 
## 
## [[3]]
## NULL

Speed overall

## [1] "DV = speed_overall"
## 
## Call:
## lm(formula = fm, data = var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.0103 -0.6989  0.0295  0.7637  3.0783 
## 
## Coefficients:
##                              Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                    8.3420     0.1657  50.336  < 2e-16 ***
## scale(agreeableness)          -0.1427     0.1948  -0.733  0.46739    
## scale(helpful_exchange)       -0.4041     0.1989  -2.032  0.04785 *  
## scale(inconsistent_codriver)  -0.1542     0.1808  -0.853  0.39815    
## scale(neuroticism_drone)       0.1797     0.1776   1.012  0.31694    
## scale(prop_female)            -0.5493     0.1735  -3.165  0.00272 ** 
## scale(switch_time_drone)       0.3961     0.1734   2.284  0.02693 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.218 on 47 degrees of freedom
## Multiple R-squared:  0.4417, Adjusted R-squared:  0.3704 
## F-statistic: 6.198 on 6 and 47 DF,  p-value: 7.598e-05
## 
##                       zero_order partial  part
## agreeableness              -0.33   -0.11 -0.08
## helpful_exchange           -0.40   -0.28 -0.22
## inconsistent_codriver      -0.27   -0.12 -0.09
## neuroticism_drone           0.27    0.15  0.11
## prop_female                -0.47   -0.42 -0.34
## switch_time_drone           0.31    0.32  0.25

## 
## Call:
## omcdiag(x = vars %>% select(n), y = vars %>% select(dv))
## 
## 
## Overall Multicollinearity Diagnostics
## 
##                        MC Results detection
## Determinant |X'X|:         0.5590         0
## Farrar Chi-Square:        29.1814         1
## Red Indicator:             0.2071         0
## Sum of Lambda Inverse:     7.2164         0
## Theil's Method:           -1.2559         0
## Condition Number:         23.7627         0
## 
## 1 --> COLLINEARITY is detected by the test 
## 0 --> COLLINEARITY is not detected by the test
## 
## 
## Call:
## imcdiag(x = vars %>% select(n), y = vars %>% select(dv))
## 
## 
## All Individual Multicollinearity Diagnostics Result
## 
##                          VIF    TOL     Wi     Fi Leamer   CVIF Klein   IND1
## agreeableness         1.3560 0.7374 3.4179 4.3614 0.8587 2.7864     0 0.0768
## helpful_exchange      1.4135 0.7074 3.9699 5.0658 0.8411 2.9045     0 0.0737
## inconsistent_codriver 1.1683 0.8560 1.6154 2.0613 0.9252 2.4006     0 0.0892
## neuroticism_drone     1.1277 0.8868 1.2257 1.5641 0.9417 2.3171     0 0.0924
## prop_female           1.0762 0.9292 0.7312 0.9330 0.9640 2.2113     0 0.0968
## switch_time_drone     1.0747 0.9305 0.7171 0.9151 0.9646 2.2083     0 0.0969
##                         IND2
## agreeableness         1.6536
## helpful_exchange      1.8426
## inconsistent_codriver 0.9072
## neuroticism_drone     0.7131
## prop_female           0.4458
## switch_time_drone     0.4378
## 
## 1 --> COLLINEARITY is detected by the test 
## 0 --> COLLINEARITY is not detected by the test
## 
## agreeableness , inconsistent_codriver , neuroticism_drone , coefficient(s) are non-significant may be due to multicollinearity
## 
## R-square of y on all x: 0.4417 
## 
## * use method argument to check which regressors may be the reason of collinearity
## ===================================
## [[1]]
## 
## Call:
## lm(formula = fm, data = var_std)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -2.99365 -0.67991  0.03463  0.76709  3.07569 
## 
## Coefficients:
##                                   Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                        8.33963    0.16891  49.374  < 2e-16 ***
## inconsistent_codriver             -0.15697    0.18448  -0.851  0.39925    
## prop_female                       -0.55078    0.17592  -3.131  0.00303 ** 
## agreeableness                     -0.14332    0.19695  -0.728  0.47050    
## helpful_exchange                  -0.40457    0.20106  -2.012  0.05007 .  
## neuroticism_drone                  0.18333    0.18253   1.004  0.32046    
## switch_time_drone                  0.39728    0.17559   2.263  0.02843 *  
## inconsistent_codriver:prop_female  0.01954    0.17689   0.110  0.91251    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.231 on 46 degrees of freedom
## Multiple R-squared:  0.4419, Adjusted R-squared:  0.3569 
## F-statistic: 5.203 on 7 and 46 DF,  p-value: 0.0002031
## 
## 
## [[2]]
## NULL
## 
## [[3]]
## 
## Call:
## lm(formula = fm, data = var_std)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.0108 -0.6706  0.0411  0.7627  3.1151 
## 
## Coefficients:
##                              Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                   8.33688    0.16993  49.061  < 2e-16 ***
## helpful_exchange             -0.40841    0.20241  -2.018  0.04947 *  
## prop_female                  -0.55348    0.17693  -3.128  0.00305 ** 
## agreeableness                -0.14074    0.19715  -0.714  0.47893    
## inconsistent_codriver        -0.15784    0.18385  -0.859  0.39505    
## neuroticism_drone             0.17975    0.17950   1.001  0.32188    
## switch_time_drone             0.39984    0.17648   2.266  0.02822 *  
## helpful_exchange:prop_female  0.03525    0.19732   0.179  0.85899    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.231 on 46 degrees of freedom
## Multiple R-squared:  0.4421, Adjusted R-squared:  0.3572 
## F-statistic: 5.208 on 7 and 46 DF,  p-value: 0.0002014

Distance overall

## [1] "DV = distance_overall"
## 
## Call:
## lm(formula = fm, data = var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2490.5  -793.7  -221.9   547.3  3811.0 
## 
## Coefficients:
##                              Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                   12642.0      185.8  68.031  < 2e-16 ***
## scale(incongruent_errors)       386.7      203.6   1.899  0.06347 .  
## scale(inconsistent_codriver)    179.3      200.7   0.893  0.37600    
## scale(switch_cost)             -415.6      203.3  -2.044  0.04630 *  
## scale(terrible_codriver)        581.7      199.7   2.913  0.00538 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1366 on 49 degrees of freedom
## Multiple R-squared:  0.3389, Adjusted R-squared:  0.285 
## F-statistic: 6.281 on 4 and 49 DF,  p-value: 0.000367
## 
##                       zero_order partial  part
## incongruent_errors          0.34    0.26  0.22
## inconsistent_codriver       0.27    0.13  0.10
## switch_cost                -0.34   -0.28 -0.24
## terrible_codriver           0.38    0.38  0.34

## 
## Call:
## omcdiag(x = vars %>% select(n), y = vars %>% select(dv))
## 
## 
## Overall Multicollinearity Diagnostics
## 
##                        MC Results detection
## Determinant |X'X|:         0.7455         0
## Farrar Chi-Square:        14.9315         1
## Red Indicator:             0.2142         0
## Sum of Lambda Inverse:     4.6313         0
## Theil's Method:           -0.4725         0
## Condition Number:          3.5825         0
## 
## 1 --> COLLINEARITY is detected by the test 
## 0 --> COLLINEARITY is not detected by the test
## 
## 
## Call:
## imcdiag(x = vars %>% select(n), y = vars %>% select(dv))
## 
## 
## All Individual Multicollinearity Diagnostics Result
## 
##                          VIF    TOL     Wi     Fi Leamer   CVIF Klein   IND1
## incongruent_errors    1.1787 0.8484 2.9776 4.5557 0.9211 1.4361     0 0.0509
## inconsistent_codriver 1.1447 0.8736 2.4110 3.6888 0.9347 1.3947     0 0.0524
## switch_cost           1.1746 0.8514 2.9094 4.4513 0.9227 1.4312     0 0.0511
## terrible_codriver     1.1334 0.8823 2.2240 3.4027 0.9393 1.3811     0 0.0529
##                         IND2
## incongruent_errors    1.1139
## inconsistent_codriver 0.9287
## switch_cost           1.0922
## terrible_codriver     0.8652
## 
## 1 --> COLLINEARITY is detected by the test 
## 0 --> COLLINEARITY is not detected by the test
## 
## incongruent_errors , inconsistent_codriver , coefficient(s) are non-significant may be due to multicollinearity
## 
## R-square of y on all x: 0.3389 
## 
## * use method argument to check which regressors may be the reason of collinearity
## ===================================